Job Description
<p><p>Join us as we work to create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all.<br/><br/>This position requires expertise in designing, developing, debugging, and maintaining AI-powered applications and data engineering workflows for both local and cloud environments.<br/><br/>The role involves working on large-scale projects, optimizing AI/ML pipelines, and ensuring scalable data infrastructure.<br/><br/>As a PMTS, you will be responsible for integrating Generative AI (GenAI) capabilities, building data pipelines for AI model training, and deploying scalable AI-powered microservices.<br/><br/>You will collaborate with AI/ML, Data Engineering, DevOps, and Product teams to deliver impactful solutions that enhance our products and services.<br/><br/>Additionally, it would be desirable if the candidate has experience in retrieval-augmented generation (RAG), fine-tuning pre-trained LLMs, AI model evaluation, data pipeline automation, and optimizing cloud-based AI :</b></p><br/><p><b>AI-Powered Software Development & API Integration :</b><br/><br/></p><p>- Develop AI-driven applications, microservices, and automation workflows using FastAPI, Flask, or Django, ensuring cloud-native deployment and performance optimization.<br/><br/></p><p>- Integrate OpenAI APIs (GPT models, Embeddings, Function Calling) and Retrieval-Augmented Generation (RAG) techniques to enhance AI-powered document retrieval, classification, and decision-making.</p><br/><p><b>Data Engineering & AI Model Performance Optimization :</b><br/><br/></p><p>- Design, build, and optimize scalable data pipelines for AI/ML workflows using Pandas, PySpark, and Dask, integrating data sources such as Kafka, AWS S3, Azure Data Lake, and Snowflake.<br/><br/></p><p>- Enhance AI model inference efficiency by implementing vector retrieval using FAISS, Pinecone, or ChromaDB, and optimize API latency with tuning techniques (temperature, top-k sampling, max tokens settings).</p><br/><p><b>Microservices, APIs & Security :</b></p><p><p><b><br/></b></p>- Develop scalable RESTful APIs for AI models and data services, ensuring integration with internal and external systems while securing API endpoints using OAuth, JWT, and API Key Authentication.<br/><br/></p><p>- Implement AI-powered logging, observability, and monitoring to track data pipelines, model drift, and inference accuracy, ensuring compliance with AI governance and security best practices.</p><br/><p><b>AI & Data Engineering Collaboration :</b><br/><br/></p><p>- Work with AI/ML, Data Engineering, and DevOps teams to optimize AI model deployments, data pipelines, and real-time/batch processing for AI-driven solutions.<br/><br/></p><p>- Engage in Agile ceremonies, backlog refinement, and collaborative problem-solving to scale AI-powered workflows in areas like fraud detection, claims processing, and intelligent Coordination and Communication :</b><br/><br/></p><p>- Collaborate with Product, UX, and Compliance teams to align AI-powered features with user needs, security policies, and regulatory frameworks (HIPAA, GDPR, SOC2).<br/><br/></p><p>- Ensure seamless integration of structured and unstructured data sources (SQL, NoSQL, vector databases) to improve AI model accuracy and retrieval efficiency.</p><br/><p><b>Mentorship & Knowledge Sharing :</b></p><p><p><b><br/></b></p>- Mentor junior engineers on AI model integration, API development, and scalable data engineering best practices, and conduct knowledge-sharing sessions.</p><br/><p><b>Education & Experience Required :</b><br/><br/></p><p>- 12-18 years of experience in software engineering or AI/ML development, preferably in AI-driven solutions.<br/><br/></p><p>- Hands-on experience with Agile development, SDLC, CI/CD pipelines, and AI model deployment lifecycles.<br/><br/></p><p>- Bachelors Degree or equivalent in Computer Science, Engineering, Data Science, or a related field.<br/><br/></p><p>- Proficiency in full-stack development with expertise in Python (preferred for AI), Java.<br/><br/></p><p>Experience with structured & unstructured data :<br/><br/></p><p>- SQL (PostgreSQL, MySQL, SQL Server).<br/><br/></p><p>- NoSQL (OpenSearch, Redis, Elasticsearch).<br/><br/></p><p>- Vector Databases (FAISS, Pinecone, ChromaDB).<br/><br/></p><p>- Cloud & AI Infrastructure<br/><br/></p><p>- AWS : Lambda, SageMaker, ECS, S3.<br/><br/></p><p>- Azure : Azure OpenAI, ML Studio.<br/><br/></p><p>- GenAI Frameworks & Tools : OpenAI API, Hugging Face Transformers, LangChain, LlamaIndex, AutoGPT, CrewAI.</p><p><br/></p><p>- Experience in LLM deployment, retrieval-augmented generation (RAG), and AI search optimization.<br/><br/></p><p>- Proficiency in AI model evaluation (BLEU, ROUGE, BERT Score, cosine similarity) and responsible AI deployment.<br/><br/></p><p>- Strong problem-solving skills, AI ethics awareness, and the ability to collaborate across AI, DevOps, and data engineering teams.<br/><br/></p><p>- Curiosity and eagerness to explore new AI models, tools, and best practices for scalable GenAI adoption.</p><br/></p> (ref:hirist.tech)